SoroushVahidi/scidocs huggingface dataset:
Machine Learning Strategies for Time Series Forecasting
(2012)

The increasing availability of large amounts of historical data and the need of performing accurate forecasting of future behavior in several scientific and applied domains demands the definition of robust and efficient techniques able to infer from observations the stochastic dependency between past and future. The forecasting domain has been influenced, from the 1960s on, by linear statistical methods such as ARIMA models. More recently, machine learning models have drawn attention and have established themselves as serious contenders to classical statistical models in the forecasting community. This chapter presents an overview of machine learning techniques in time series forecasting by focusing on three aspects: the formalization of one-step forecasting problems as supervised learning tasks, the discussion of local learning techniques as an effective tool for dealing with temporal data and the role of the forecasting strategy when we move from one-step to multiple-step forecasting.
A qualitative study on factors that influence women’s choice of delivery in health facilities in Addis Ababa, Ethiopia
(2016)

BACKGROUND
Facility based delivery for mothers is one of the proven interventions to reduce maternal and neonatal morbidity and mortality. This study identified women's reasons for seeking to give birth in a health facility and captured their perceptions of the quality of care they received during their most recent birth, in a population with high utilization of facility based deliveries.


METHODS
This qualitative study was conducted in eight health centers in Addis Ababa. Women bringing their index child for first vaccinations were invited to participate in an in-depth interview about their last delivery. Sixteen in-depth interviews were conducted. Interviews were conducted by trained researchers using a semi-structured interview guide. The data were transcribed verbatim in Amharic and translated into English. A thematic analysis was conducted to answer specific study questions.


RESULTS
All research participants expressed a preference for facility based delivery because of their awareness of obstetric complications, and related perceptions that facility-birth is safer for the mother and child. Dimensions of quality of care and the cost of services were identified as influencing decisions about whether to seek care in the public or private sector. Media campaigns, information from social networks and women's experiences with healthcare providers and facilities influenced care-seeking decisions.


CONCLUSIONS
The universal preference for facility-based birth by women in this study indicates that, in Addis Ababa, facility based delivery has become a preferred norm. Sources of information for decision-making and the dimensions of quality prioritized by women should be taken into account to develop interventions to promote facility-based births in other settings.
Compressive Sensing Image Sensors-Hardware Implementation
(2013)

The compressive sensing (CS) paradigm uses simultaneous sensing and compression to provide an efficient image acquisition technique. The main advantages of the CS method include high resolution imaging using low resolution sensor arrays and faster image acquisition. Since the imaging philosophy in CS imagers is different from conventional imaging systems, new physical structures have been developed for cameras that use the CS technique. In this paper, a review of different hardware implementations of CS encoding in optical and electrical domains is presented. Considering the recent advances in CMOS (complementary metal-oxide-semiconductor) technologies and the feasibility of performing on-chip signal processing, important practical issues in the implementation of CS in CMOS sensors are emphasized. In addition, the CS coding for video capture is discussed.
Partitioning Well-Clustered Graphs with k-Means and Heat Kernel
(2014)

We study a suitable class of well-clustered graphs that admit good k-way partitions and present the first almost-linear time algorithm for with almost-optimal approximation guarantees partitioning such graphs. A good k-way partition is a partition of the vertices of a graph into disjoint clusters (subsets) {Si}i=1, such that each cluster is better connected on the inside than towards the outside. This problem is a key building block in algorithm design, and has wide applications in community detection and network analysis. Key to our result is a theorem on the multi-cut and eigenvector structure of the graph Laplacians of these well-clustered graphs. Based on this theorem, we give the first rigorous guarantees on the approximation ratios of the widely used k-means clustering algorithms. We also give an almost-linear time algorithm based on heat kernel embeddings and approximate nearest neighbor data structures.
Speaker identification on the SCOTUS corpus
(2007)

This paper reports the results of our experiments on speaker identification in the SCOTUS corpus, which includes oral arguments from the Supreme Court of the United States. Our main findings are as follows: 1) a combination of Gaussian mixture models and monophone HMM models attains near-100% textindependent identification accuracy on utterances that are longer than one second; 2) the sampling rate of 11025 Hz achieves the best performance (higher sampling rates are harmful); and a sampling rate as low as 2000 Hz still achieves more than 90% accuracy; 3) a distance score based on likelihood numbers was used to measure the variability of phones among speakers; we found that the most variable phone is the phone UH (as in good), and the velar nasal NG is more variable than the other two nasal sounds M and N; 4.) our models achieved “perfect” forced alignment on very long speech segments (40 minutes). These findings and their significance are discussed.
Diffusion-on-Manifold Aggregation of Local Features for Shape-based 3D Model Retrieval
(2015)

Aggregating a set of local features has become one of the most common approaches for representing a multi-media data such as 2D image and 3D model. The success of Bag-of-Features (BF) aggregation [2] prompted several extensions to BF, that are, VLAD [12], Fisher Vector (FV) coding [22] and Super Vector (SV) coding [34]. They all learn small number of codewords, or representative local features, by clustering a set of large number of local features. The set of local features extracted from a media data (e.g., an image) is encoded by considering distribution of features around the codewords; BF uses frequency, VLAD and FV uses displacement vector, and SV uses a combination of both. In doing so, these encoding algorithms assume linearity of feature space about a codeword. Consequently, even if the set of features form a non-linear manifold, its non-linearity would be ignored, potentially degrading quality of aggregated features. In this paper, we propose a novel feature aggregation algorithm called Diffusion-on-Manifold (DM) that tries to take into account, via diffusion distance, structure of non-linear manifold formed by the set of local features. In view of 3D shape retrieval, we also propose a local 3D shape feature defined for oriented point set. Experiments using shape-based 3D model retrieval scenario show that the DM aggregation results in better retrieval accuracy than the existing aggregation algorithms we've compared against, that are, VLAD, FV, and SV, etc..
Fractional-Slot Concentrated-Windings Synchronous Permanent Magnet Machines: Opportunities and Challenges
(2010)

Fractional-slot concentrated-winding (FSCW) synchronous permanent magnet (PM) machines have been gaining interest over the last few years. This is mainly due to the several advantages that this type of windings provides. These include high-power density, high efficiency, short end turns, high slot fill factor particularly when coupled with segmented stator structures, low cogging torque, flux-weakening capability, and fault tolerance. This paper is going to provide a thorough analysis of FSCW synchronous PM machines in terms of opportunities and challenges. This paper will cover the theory and design of FSCW synchronous PM machines, achieving high-power density, flux-weakening capability, comparison of single- versus double-layer windings, fault-tolerance rotor losses, parasitic effects, comparison of interior versus surface PM machines, and various types of machines. This paper will also provide a summary of the commercial applications that involve FSCW synchronous PM machines.
Comparison between SPM and IPM motor drives for EV application
(2010)

Electric Vehicles make use of permanent magnet synchronous traction motors for their high torque density and efficiency. A comparison between interior permanent magnet (IPM) and surface mounted permanent magnet (SPM) motors is carried out, in terms of performance at given inverter ratings. A simplified analytical approach is proposed and its results are confirmed by actual motor design, validated through FEA. The main result of the proposed analysis is to put in evidence the different behavior of IPM and SPM motors in terms of overload capability: as expected, the overload current results in a power overload of the IPM motor, while in the SPM motor the maximum power is upper limited irrespectively of the current overload.
A new structure of universal motor using soft magnetic composites
(2001)

The universal or ac commutator motor, widely used in hand tools and domestic appliances generally uses a two-pole stator with a concentrated winding and an armature with interlocked coils elements. The copper volume and the axial length of the end windings of such conventional structures are then usually very important. In this paper, the authors present a new universal motor structure based on an efficient use of the isotropic magnetic properties of the soft magnetic composites and on the concentrated winding technique. The stator core presents a claw-pole structure and the armature has a concentrated winding with several coils wound around the same tooth. With this new ac commutator motor structure, a reduction of the total volume by a ratio equal to 200% is obtained when compared to a classical universal motor structure with nearly identical performance.
Comparison of synchronous PM machine types for wide constant-power speed range operation
(2005)

This paper presents a detailed comparison of the high-speed operating characteristics of four synchronous PM machines for applications that require wide speed ranges of constant-power operation. These machines include surface PM machines with both distributed and fractional-slot concentrated windings, and two interior PM machine with distributed windings. These two versions of the interior PM machine include one with and a tight constraint on the machine's back-emf voltage at top speed and one without this constraint The target application is an automotive direct-drive starter/alternator requiring a very wide 10:1 constant power speed ratio (CPSR). Detailed comparisons of the performance characteristics of the machines are presented that include important issues such as the back-emf voltage at top speed, machine mass and cost, and eddy current losses in the magnets. Analytical results are verified using finite element analysis (FEA). Guidelines are developed to help drive designers decide which type of machine is most suitable for high-CPSR applications. Tradeoffs associated with choosing each of these machines are presented.
Electrical machine topologies and technologies for electric, hybrid, and fuel cell vehicles
(2008)

This paper overviews various electrical machine technologies, including brushless permanent magnet, induction, switched reluctance, and brushless reluctance machines, with particular emphasis on the concept of hybrid electrical machine topologies, and compares their relative merits, in terms of torque and power density, operating speed range, over load capability, and efficiency, for application in electric, hybrid and fuel cell vehicles. electric vehicles.
Spacer-First Damascene-Gate FinFET Architecture Featuring Stringer-Free Integration
(2007)

This letter presents a new Damascene-gate FinFET process that inherently suppresses stringers, resulting from gate and spacers patterning. The so-called spacer-first integration scheme relies on the engineering of a hydrogen silsesquioxane layer by electron beam lithography followed by two selective compartmentalized development steps to successively release the Damascene-gate cavity and the source/drain (S/D) contact regions. In contrast to the existing gate-first and gate-last integration approaches, the resulting FinFET process does not impose any restriction or interdependency on the sizing of the fins, gate, spacers, and S/D regions. A complete morphological and electrical validation is proposed in the particular case of wrap-around self-aligned metallic Schottky S/D contacts.
Thickness Reduction and Performance Enhancement of Steerable Square Loop Antenna Using Hybrid High Impedance Surface
(2010)

In order to reduce the thickness of a steerable beam square loop antenna, the effects of combining it with various periodic high impedance surfaces (HISs) are investigated. When a via-less HIS is used, the radiation pattern has high side lobes, which are shown to be due to surface waves propagating in the HIS lattice. Using a HIS with vias between the plates and ground removes the surface waves, but the beam is distorted due to strong coupling between the HIS's vias and the antenna element. Consequently a hybrid HIS is designed. This uses a via-less lattice beneath the loop, with vias at the edge of the HIS to suppress surface wave propagation. Consequently, a square loop antenna with four feeds on a hybrid HIS substrate is proposed for beam steering applications. This antenna has a low-profile with a total thickness of 4.69 mm for a test frequency band of 4.3 GHz to 5.0 GHz. It exhibits a gain of 8.65 dB at the test frequency (4.7 GHz). Compared to the earlier reported steerable square loop antenna, the new antenna achieves a 61% reduction in substrate thickness, a bandwidth enhancement by 150 MHz and an increase in gain by 1.95 dB.
Performing edge detection by difference of Gaussians using q-Gaussian kernels
(2013)

X iv :1 31 1. 25 61 v2 [ cs .C V ] 1 2 N ov 2 01 3 Performing edge detection by difference of Gaussians using q-Gaussian kernels Lucas Assirati, Núbia R. da Silva, 2 Lilian Berton, Alneu de A. Lopes, and Odemir M. Bruno 2 Scientific Computing Group, São Carlos Institute of Physics, University of São Paulo (USP), cx 369 13560-970 São Carlos, São Paulo, Brazil www.scg.ifsc.usp.br Institute of Mathematics and Computer Science, University of São Paulo (USP), Avenida Trabalhador são-carlense, 40
Cyber Defense Capability Model: A Foundation Taxonomy
(2015)

Cyber attacks have significantly increased over the last few years, where the attackers are highly skilled, more organized and supported by other powerful actors to devise attacks towards specific targets. To aid the development of a strategic plan to defend against emerging attacks, we present a high-level taxonomy along with a cyber defense model to address the interaction and relationships between taxonomy elements. A cyber-kinetic reference model which is used widely by U.S Air Force is adopted as a baseline for the model and taxonomy development. Asset, Cyber Capability, and Preparation Process are the three high-level elements that are presented for the cyber defense capability model. The Cyber Capability, as the focal point of the study, uses three classifiers to characterize the strategic cyber defense mechanisms, which are classified by active, passive and collaborative defense. To achieve a proper cyber defense strategy, the key actors, assets and associated preparation procedure are identified. Finally, the proposed taxonomy is extensible so that additional dimensions or classifications can be added to future needs.
Practical Privacy-Preserving Medical Diagnosis Using Homomorphic Encryption
(2016)

The use of remote services offered by cloud providers have been popular in the last lustrum. Services allow users to store remote files, or to analyze data for several purposes, like health-care or message analysis. However, when personal data are sent to the Cloud, users may lose privacy on the data-content, and on the other side cloud providers may use those data for their own businesses. In this paper, we present our solution to analyze users health-data directly into the Cloud while preserving users privacy. Our solution makes use of homomorphic encryption to protect users data during the analysis. In particular, we developed a mobile application that offloads users data into the Cloud, and a homomorphic encryption algorithm that processes those data without leaking any information to the Cloud provider. Performed empirical tests show that our HE algorithm is able to evaluate users data in reasonable time proving the feasibility of this emerging way of private-data evaluation.
Explaining consumer satisfaction of services: The role of innovativeness and emotion in an electronic mediated environment
(2015)

a r t i c l e i n f o The services section has started to dominate economic activity in many industrialized economies since the last decade. The growth of services in Electronic Mediated Environment (EME) has changed the manner in which firms and consumers interact. Drawing on such theoretical foundations as trust, risk perceptions, trying, emotion, and satisfaction, this study proposes an integrative research model to investigate the relationships among these constructs in the EME context. We collected data from 415 consumers and found that consumer innovativeness and emotion are significant antecedents of trust, risk perception of service, perceived benefits, and service quality. These important factors will influence consumer satisfaction in EME. This empirical investigation breaks a new ground for future studies to understand and gauge the influence of innovativeness and emotion in emerging IT artifacts such as EME services. The service sector has become quantitatively the most essential sector in the world economy over the last decade. It now leads economic activities in a wide variety of industrialized economies. According to CIA World Factbook [14], the service industry accounted for 76.8% and 73.1% of the U.S. and European Union gross domestic product in 2011, respectively. Concurrently, there are more service companies on the list of Future 500 companies than before. Given the precipitously growing rate of the service section, technologically empowered advancements of information technology (IT) and the ever developing Internet are constantly galvanizing today's organizations to more effectively and efficiently coordinate with their constituents via the value-added electronic services (e-services) conduit. Furthermore, the mushrooming growth of e-commerce reflects the increasingly paramount role of IT in e-services while IT spurs the influx of e-service economy across the Internet. It is apparent that Information Systems (IS) plays a crucial role in EME because contemporary organizations have to leverage IT to improve business performance and assist the communication and service delivery amid various value-chain constituents. Consumers play a focal role in the paradigmatic change from the exchange of goods towards a service-centered model of exchange as found in both IS and marketing literature [73]. In this study, the conceptual definition of services in EME is based on Dai and Salam [19] and thereafter refers to the " services being facilitated by IT where the consumer interacts with a proper user interface including web site, mobile phone, social media, virtual world environment, tablets, etc., so as to gain a consumption …
ITALICA at PAN 2013: An Ensemble Learning Approach to Author Profiling Notebook for PAN at CLEF 2013
(2013)

This notebook discusses the approach to the Author Profiling task developed by the Italica group for PAN 2013. This system implements two different sets of classifiers which are combined later in order to build a final classifier that takes into account the decisions of the previous ones. The initial classifiers are focused on vector space representations of the documents as a bag of words and n-grams of POS tags and also on a set of stylistic features of the texts. The final classifier consists of a stacking schema that combines the other ones. This approach has obtained better results for the Spanish dataset than for the English dataset, probably due to the use of more detailed POS tagset in the former.
An extension of the GQM+Strategies approach with formal causal reasoning
(2017)

Context: Successful organizations need to manage and synchronize their strategic objectives with daily operations and activities. In general, achieving that requires a continuous process of organizational alignment. GQM + Strategies is an approach that helps software organizations with documenting and aligning organizational goals and strategies, and developing measurement programs. Objective: In this paper, the GQM + Strategies approach is further evolved and extended to include capabilities to evaluate the relationships of organizational goals and strategies through causal analysis. Method: We used an analytical paradigm to develop a formal causal model over the GQM + Strategies structure. In addition, an empirical pre-experimental study was designed to test practitioners’ abilities to provide necessary input for the formal causal model. Results: A developed formal causal model over the GQM + Strategies structure allows the use of causal reasoning for the purpose of analyzing dependencies among chosen sets of goals. We illustrate this by showing how to analyze the impact of risky goals on other goals in the grid. The results of the empirical study showed that the practitioners had no difficulties providing their predictions, i.e. inputs into the causal model. Conclusion: The proposed solution extends the existing GQM + Strategies knowledge base by further elaborating and clarifying the process of creating grids by introducing causality theory. The use of causality theory allows experts to quantify their knowledge and beliefs regarding the effectiveness of organizational strategies. As a part of future work, formal causal models and causal reasoning can be implemented as a supporting software tool for the GQM + Strategies approach. © 2017 Elsevier B.V. All rights reserved.
Recommender System Based on Product Taxonomy in E-Commerce Sites
(2013)

This article describes a novel and fast recommender system for websites based on product taxonomy and user click patterns. The proposed system consists of the following four steps. First, a product-preference matrix for each customer is estimated through a linear combination of the click, basket placement, and purchase statuses. Second, the preference matrix of the genre and that of the specific type are constructed by computing the ratios of the number of clicks, basket placements, and purchases of a product to the totals. Third, cluster analysis is performed using the genre preference matrix, and a neighborhood formation process is conducted using the specific-type preference matrix. Finally, data are generated for prediction, in which a customer’s preference for specific types is greater than a given threshold value. Using these data sets, computational burden and processing time are greatly reduced. The effectiveness of the proposed approach was assessed by applying the F1 metric to an experimental e-commerce website. The proposed method outperformed conventional methods.